N. Serap Sengor

An efficient inverse ANN modeling approach using prior knowledge input with difference method

Artificial Neural Networks (ANN) have emerged as a powerful technique for modeling. Since the embedding knowledge in ANN models is possible by the Knowledge Based ANN (KBANN) methods, more accurate results than classical ANN approach can be obtained with KBANN. Source Difference (SD), Prior Knowledge Input (PKI) and Prior Knowledge Input with Difference (PKI-D) are several methods to be mentioned which combines existing knowledge with ANN methods. The existing knowledge is obtained either by mathematical formulations, ANN modeling or measured data. The Prior Knowledge Input with Difference, which is the latest method amongst KBANN approaches is discussed in this work. We compared the response efficiency and time consumption performances of PKI-D and classical ANN methods to obtain model for Inverse Scattering Problem.

A dynamical model of a cognitive function: Action selection

Abstract A model of cortex-basal ganglia-thalamus-cortex loop is presented. Even though the mentioned loop has been proposed to take part in different cognitive processes, the model exploits only the “action selection” function. The analysis of the model is based on the stability analysis of a non-linear dynamical system. Thus a biologically valid model of a cognitive function is given and its analysis is accomplished using system theory tools.

Key-frame based video fingerprinting by NMF

This paper presents a key-frame based video fingerprinting method in which the fingerprint matching is modeled as a two hypothesis testing problem. The perceptual fingerprints that uniquely identify the video content are extracted by non-negative matrix factorization (NMF) via Gaussian weighting in order to assure compactness and robustness to global luminance distortions. The system performance is further improved to enhance its robustness to geometric attacks by integrating the transform invariant NMF (T-NMF) indicies into the matching scheme. The overall performance is evaluated on TRECVID video sequences. It is shown that the proposed video fingerprinting method is highly robust to global attacks described by TRECVID and it can also handle the geometric attacks for the transformations used at indexing phase.

A computational model for the effect of dopamine on action selection during stroop test

Based on a connectionist model of cortex-basal ganglia-thalamus loop recently proposed by authors, a simple connectionist model realizing the Stroop effect is established. The connectionist model of cortex-basal ganglia-thalamus loop is a nonlinear dynamical system and the model is not only capable of revealing the action selection property of basal ganglia but also is capable of modelling the effect of dopamine on action selection. While the interpretation of action selection function is based on solutions of nonlinear dynamical system, the effect of dopamine is modelled by a parameter. The effect of dopamine in inhibiting the habitual behaviour corresponding to word reading in Stroop test and letting the novel one occur corresponding to colour naming is investigated using the model established in this work.

From occasional choices to inevitable musts: a computational model of nicotine addiction

Although, there are considerable works on the neural mechanisms of reward-based learning and decision making, and most of them mention that addiction can be explained by malfunctioning in these cognitive processes, there are very few computational models. This paper focuses on nicotine addiction, and a computational model for nicotine addiction is proposed based on the neurophysiological basis of addiction. The model compromises different levels ranging from molecular basis to systems level, and it demonstrates three different possible behavioral patterns which are addict, nonaddict, and indecisive. The dynamical behavior of the proposed model is investigated with tools used in analyzing nonlinear dynamical systems, and the relation between the behavioral patterns and the dynamics of the system is discussed.

Investigating the synchronization of cortical neurons using BRIAN simulator

Looking at the computational properties of brain has been inspiring in constructing intelligent systems. So, trying to mimic the brains computational properties with simple models could give rise to new approaches. Here, the role of connection weights on the synchronization of cortex oscillations is investigated. To carry out this investigation, networks inspired by the structure of cortex are built using a simple neuron model. The simulations are carried out in BRIAN, which is a simulation environment using Python programming language and it is created to provide a workspace on spiking neural networks.

Modeling cortical states by spiking neurons

Computational neuroscience has been effective in providing tools for investigation of cognitive processes along with neurodegenerative diseases and neurological disorders, recently. In this work, it will be shown that it could be possible to understand the mechanisms behind cortical dynamics using spiking neural networks. Here, Izhikevich neuron model is used to set up a cortex model and with this model, signals related to brain dynamics have been observed.

Convergence of threshold networks using their dissipative system model

A new dynamical energy system model representation is given for threshold networks. Inspired by the relation between stability and dissipativeness of dynamical systems, the convergence property of threshold networks is investigated.Using the energy function inherent within the given model a condition, namely the dissipativeness of the dynamical system, necessary and sufficient condition for the convergence of the threshold network to a fixed point, is given. Also, an easy to check inequality is stated to test the convergence of the threshold network.

Discrete-time version of Kalman-Yacubovitch-Popov lemma for non-linear systems

Kalman-Yacubovitch-Popov Lemma shows how the passivity and stability concepts are related and inspires the way to generating Lyapunov functions for linear systems. In this paper a version of the lemma is given for nonlinear discrete time systems.Kalman-Yacubovitch-Popov Lemma shows how the passivity and stability concepts are related and inspires the way to generating Lyapunov functions for linear systems. In this paper a version of the lemma is given for nonlinear discrete time systems.